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Machine Learning Techniques for Online Social Networks

Overview of attention for book
Cover of 'Machine Learning Techniques for Online Social Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Acceleration of Functional Cluster Extraction and Analysis of Cluster Affinity
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    Chapter 2 δ -Hyperbolicity and the Core-Periphery Structure in Graphs
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    Chapter 3 A Framework for OSN Performance Evaluation Studies
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    Chapter 4 On the Problem of Multi-Staged Impression Allocation in Online Social Networks
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    Chapter 5 Order-of-Magnitude Popularity Estimation of Pirated Content
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    Chapter 6 Learning What to Share in Online Social Networks Using Deep Reinforcement Learning
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    Chapter 7 Centrality and Community Scoring Functions in Incomplete Networks: Their Sensitivity, Robustness, and Reliability
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    Chapter 8 Ameliorating Search Results Recommendation System Based on K -Means Clustering Algorithm and Distance Measurements
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    Chapter 9 Dynamics of Large-Scale Networks Following a Merger
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    Chapter 10 Cloud Assisted Personal Online Social Network
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    Chapter 11 Text-Based Analysis of Emotion by Considering Tweets
Attention for Chapter 7: Centrality and Community Scoring Functions in Incomplete Networks: Their Sensitivity, Robustness, and Reliability
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Chapter title
Centrality and Community Scoring Functions in Incomplete Networks: Their Sensitivity, Robustness, and Reliability
Chapter number 7
Book title
Machine Learning Techniques for Online Social Networks
Published by
Springer, Cham, January 2018
DOI 10.1007/978-3-319-89932-9_7
Book ISBNs
978-3-31-989931-2, 978-3-31-989932-9
Authors

Soumya Sarkar, Suhansanu Kumar, Sanjukta Bhowmick, Animesh Mukherjee, Sarkar, Soumya, Kumar, Suhansanu, Bhowmick, Sanjukta, Mukherjee, Animesh

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 100%
Readers by discipline Count As %
Unspecified 1 100%

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